peer reviewed

peer reviewed

Among the reported solutions to the class imbalance issue, the undersampling approaches, which remove instances of insignificant samples from the majority class, are quite prevalent. However, the undersampling approaches may discard significant patterns in the datasets. A prototype, which is always an actual sample from the data, represents a group of samples in the dataset. Our hypothesis is that prototypes can fill the missing significant patterns that are discarded by undersampling methods and help to improve model performance. To confirm our intuition, we articulate prototypes to undersampling methods in the machine learning pipeline. We show that there is a statistically significant difference between the AUPR and AUROC results of undersampling methods and our approach.

Pre-trained Language Models such as BERT have become ubiquitous in NLP where they have achieved state-of-the-art performance in most NLP tasks. While these models are readily available for English and other widely spoken languages, they remain scarce for low-resource languages such as Luxembourgish. In this paper, we present LuxemBERT, a BERT model for the Luxembourgish language that we create using the following approach: we augment the pre-training dataset by considering text data from a closely related language that we partially translate using a simple and straightforward method. We are then able to produce the LuxemBERT model, which we show to be effective for various NLP tasks: it outperforms a simple baseline built with the available Luxembourgish text data as well the multilingual mBERT model, which is currently the only option for transformer-based language models in Luxembourgish. Furthermore, we present datasets for various downstream NLP tasks that we created for this study and will make available to researchers on request.

Credit scoring systems are critical FinTech applications that concern the analysis of the creditworthiness of a person or organization. While decisions were previously based on human expertise, they are now increasingly relying on data analysis and machine learning. In this paper, we assess the ability of state-of-the-art adversarial machine learning to craft attacks on a real-world credit scoring system. Interestingly, we find that, while these techniques can generate large numbers of adversarial data, these are practically useless as they all violate domain-specific constraints. In other words, the generated examples are all false positives as they cannot occur in practice. To circumvent this limitation, we propose CoEvA2, a search-based method that generates valid adversarial examples (satisfying the domain constraints). CoEvA2 utilizes multi-objective search in order to simultaneously handle constraints, perform the attack and maximize the overdraft amount requested. We evaluate CoEvA2 on a major bank's real-world system by checking its ability to craft valid attacks. CoEvA2 generates thousands of valid adversarial examples, revealing a high risk for the banking system. Fortunately, by improving the system through adversarial training (based on the produced examples), we increase its robustness and make our attack fail.

In this paper, we present an approach to discover periodic-frequent multi-step human routines in event data from smart devices and sensors deployed at home 1. Based on the discovered routines, our approach is able to suggest rules to automate the control of different aspects of the home environment. We evaluate our approach through an in the lab study, a study based on synthetic data, and an in-the-wild study. Our results show that our approach exhibits a high recall-precision performance, with a recovery rate of around 90% for most of the cases under investigation.

In this paper, we propose a new approach capable of performing continuous identification of users in home and office environments based on hand and arm motion patterns obtained from a wrist-worn inertial measurement unit (IMU). Different from state-of-the-art methods, our approach is not constrained to particular types of movements, gestures, or activities, thus allowing users to perform freely and unconstrained their daily routines while the identification takes place. We evaluate our approach by conducting an in the lab study and two in-situ studies, one in home environment and one in office environment. Our studies involved a total of 29 different participants and the data collected corresponds to approximately 256 hours. The results obtained in the studies indicate that our approach is able to perform continuous user identification with an accuracy of 0.88 for office environments and 0.71 for the average size of a household.

High-level context awareness can be significantly improved by the recognition of real-life situations. The theory of context spaces is a context awareness approach that uses spatial metaphors to provide integrated mechanisms for both low-level and high-level context awareness and situation awareness. Taking context spaces theory situation awareness as a baseline, we propose and analyze the enhanced situation awareness techniques, which allow us to reason about broad class of real-life situations. We also improve reasoning about the relationships between situations, and discuss how it relates to newly proposed situation awareness approaches. Practical evaluation of the results is also discussed.